Overview

Dataset statistics

Number of variables12
Number of observations338
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.8 KiB
Average record size in memory96.4 B

Variable types

Numeric11
Categorical1

Warnings

Group1 is highly correlated with Group3 and 1 other fieldsHigh correlation
Group3 is highly correlated with Group1 and 1 other fieldsHigh correlation
Group4 is highly correlated with Group1 and 1 other fieldsHigh correlation
Group7 is highly correlated with Group8High correlation
Group8 is highly correlated with Group7High correlation
Group7 has 6 (1.8%) zeros Zeros
Group8 has 6 (1.8%) zeros Zeros

Reproduction

Analysis started2021-02-06 08:18:34.460021
Analysis finished2021-02-06 08:18:47.615550
Duration13.16 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Group1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct292
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3488022715
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:47.708420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1285460741
Q10.2300866108
median0.3120829192
Q30.4463770174
95-th percentile0.6332528752
Maximum1
Range1
Interquartile range (IQR)0.2162904066

Descriptive statistics

Standard deviation0.1664523615
Coefficient of variation (CV)0.4772112315
Kurtosis0.6268529636
Mean0.3488022715
Median Absolute Deviation (MAD)0.09844289839
Skewness0.8064225166
Sum117.8951678
Variance0.02770638866
MonotocityNot monotonic
2021-02-06T08:18:47.854187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27966302243
 
0.9%
0.24605991763
 
0.9%
0.20062473382
 
0.6%
0.21056368022
 
0.6%
0.25931184632
 
0.6%
0.34260968342
 
0.6%
0.13701547642
 
0.6%
0.25173931562
 
0.6%
0.22381560892
 
0.6%
0.5877703632
 
0.6%
Other values (282)316
93.5%
ValueCountFrequency (%)
01
0.3%
0.057503904591
0.3%
0.058592455871
0.3%
0.076482559521
0.3%
0.076529887831
0.3%
ValueCountFrequency (%)
11
0.3%
0.95787779831
0.3%
0.86322116521
0.3%
0.81731269821
0.3%
0.77187751431
0.3%

Group2
Real number (ℝ≥0)

Distinct313
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3181308819
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:48.004756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1096550558
Q10.2101792357
median0.3106188705
Q30.4020121745
95-th percentile0.5616165032
Maximum1
Range1
Interquartile range (IQR)0.1918329388

Descriptive statistics

Standard deviation0.1425842437
Coefficient of variation (CV)0.4481936581
Kurtosis1.602154631
Mean0.3181308819
Median Absolute Deviation (MAD)0.09773419006
Skewness0.6847854524
Sum107.5282381
Variance0.02033026656
MonotocityNot monotonic
2021-02-06T08:18:48.154366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23537368962
 
0.6%
0.22590463312
 
0.6%
0.25566452492
 
0.6%
0.34731146432
 
0.6%
0.39026039912
 
0.6%
0.34325329732
 
0.6%
0.30064254312
 
0.6%
0.18498478192
 
0.6%
0.35644234022
 
0.6%
0.4206966522
 
0.6%
Other values (303)318
94.1%
ValueCountFrequency (%)
01
0.3%
0.022658099431
0.3%
0.034156239431
0.3%
0.037538045321
0.3%
0.039905309441
0.3%
ValueCountFrequency (%)
11
0.3%
0.81501521811
0.3%
0.80656070341
0.3%
0.76969901931
0.3%
0.72404463981
0.3%

Group3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct318
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3438777167
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:48.299052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1166574528
Q10.2234468938
median0.3019141732
Q30.4518347039
95-th percentile0.6282219612
Maximum1
Range1
Interquartile range (IQR)0.2283878101

Descriptive statistics

Standard deviation0.1676946891
Coefficient of variation (CV)0.4876579115
Kurtosis0.7170283668
Mean0.3438777167
Median Absolute Deviation (MAD)0.09705618133
Skewness0.8515998959
Sum116.2306682
Variance0.02812150875
MonotocityNot monotonic
2021-02-06T08:18:48.444987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.24462718542
 
0.6%
0.30004837262
 
0.6%
0.59574321062
 
0.6%
0.21373782052
 
0.6%
0.50867251742
 
0.6%
0.62822196122
 
0.6%
0.40570796772
 
0.6%
0.45269850042
 
0.6%
0.48103102762
 
0.6%
0.52802156042
 
0.6%
Other values (308)318
94.1%
ValueCountFrequency (%)
01
0.3%
0.054730149961
0.3%
0.065510331011
0.3%
0.071176836431
0.3%
0.072904429551
0.3%
ValueCountFrequency (%)
11
0.3%
0.95577361621
0.3%
0.8825236681
0.3%
0.84589869391
0.3%
0.79545297491
0.3%

Group4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct324
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2255673804
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:48.592768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06084695394
Q10.1226172787
median0.1794735725
Q30.2920292931
95-th percentile0.4871789429
Maximum1
Range1
Interquartile range (IQR)0.1694120144

Descriptive statistics

Standard deviation0.1483061184
Coefficient of variation (CV)0.6574803418
Kurtosis3.1620554
Mean0.2255673804
Median Absolute Deviation (MAD)0.07060072172
Skewness1.479631604
Sum76.24177457
Variance0.02199470474
MonotocityNot monotonic
2021-02-06T08:18:48.751387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15652727663
 
0.9%
0.39588197832
 
0.6%
0.47569518152
 
0.6%
0.17648057742
 
0.6%
0.15983867542
 
0.6%
0.15402250052
 
0.6%
0.395457442
 
0.6%
0.42220335392
 
0.6%
0.18645722782
 
0.6%
0.17724474632
 
0.6%
Other values (314)317
93.8%
ValueCountFrequency (%)
01
0.3%
0.024793037571
0.3%
0.02564211421
0.3%
0.032986627041
0.3%
0.033241350031
0.3%
ValueCountFrequency (%)
11
0.3%
0.89428995971
0.3%
0.73636170661
0.3%
0.68669072381
0.3%
0.65485035021
0.3%

Group5
Real number (ℝ≥0)

Distinct291
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4249746762
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:48.903575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.173044166
Q10.3051770288
median0.4210974571
Q30.5349190899
95-th percentile0.6875532303
Maximum1
Range1
Interquartile range (IQR)0.2297420611

Descriptive statistics

Standard deviation0.1693382378
Coefficient of variation (CV)0.3984666552
Kurtosis0.2989041036
Mean0.4249746762
Median Absolute Deviation (MAD)0.1153424991
Skewness0.3398898821
Sum143.6414406
Variance0.02867543877
MonotocityNot monotonic
2021-02-06T08:18:49.050945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.66297603113
 
0.9%
0.46465506753
 
0.9%
0.53643995623
 
0.9%
0.39068013143
 
0.9%
0.63864217063
 
0.9%
0.43557610413
 
0.9%
0.62769193333
 
0.9%
0.52183963993
 
0.9%
0.61917508212
 
0.6%
0.4853388492
 
0.6%
Other values (281)310
91.7%
ValueCountFrequency (%)
01
0.3%
0.02165713591
0.3%
0.039542523421
0.3%
0.076894999391
0.3%
0.085046842681
0.3%
ValueCountFrequency (%)
11
0.3%
0.97323275341
0.3%
0.94038204161
0.3%
0.86373038081
0.3%
0.85278014361
0.3%

Group6
Real number (ℝ≥0)

Distinct328
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2715519322
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:49.193821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05805932151
Q10.1442166125
median0.2401079688
Q30.3538739955
95-th percentile0.5959296976
Maximum1
Range1
Interquartile range (IQR)0.209657383

Descriptive statistics

Standard deviation0.172008515
Coefficient of variation (CV)0.6334276969
Kurtosis1.527114084
Mean0.2715519322
Median Absolute Deviation (MAD)0.1058217287
Skewness1.128597283
Sum91.78455309
Variance0.02958692924
MonotocityNot monotonic
2021-02-06T08:18:49.336290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.34083798542
 
0.6%
0.40555794122
 
0.6%
0.46199619662
 
0.6%
0.3411447152
 
0.6%
0.26170173612
 
0.6%
0.32918225882
 
0.6%
0.43101650212
 
0.6%
0.2905343232
 
0.6%
0.31047175022
 
0.6%
0.23222501692
 
0.6%
Other values (318)318
94.1%
ValueCountFrequency (%)
01
0.3%
0.012453223731
0.3%
0.022605975091
0.3%
0.036132752591
0.3%
0.039077357221
0.3%
ValueCountFrequency (%)
11
0.3%
0.89571191951
0.3%
0.81994969631
0.3%
0.81136126621
0.3%
0.80921415861
0.3%

Group7
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct327
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2237705167
Minimum0
Maximum1
Zeros6
Zeros (%)1.8%
Memory size2.8 KiB
2021-02-06T08:18:49.477899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01020196813
Q10.07152061856
median0.1690253046
Q30.330131209
95-th percentile0.5749414246
Maximum1
Range1
Interquartile range (IQR)0.2586105904

Descriptive statistics

Standard deviation0.1934361914
Coefficient of variation (CV)0.8644400269
Kurtosis1.830115208
Mean0.2237705167
Median Absolute Deviation (MAD)0.1131326148
Skewness1.28372811
Sum75.63443463
Variance0.03741756016
MonotocityNot monotonic
2021-02-06T08:18:50.038473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
1.8%
0.46251171512
 
0.6%
0.26124648552
 
0.6%
0.25843486412
 
0.6%
0.2820993442
 
0.6%
0.56630740392
 
0.6%
0.046204311152
 
0.6%
0.0022813964391
 
0.3%
0.003622305531
 
0.3%
0.16033270851
 
0.3%
Other values (317)317
93.8%
ValueCountFrequency (%)
06
1.8%
0.0016213683221
 
0.3%
0.0022813964391
 
0.3%
0.0034231490161
 
0.3%
0.0034840674791
 
0.3%
ValueCountFrequency (%)
11
0.3%
0.99906279291
0.3%
0.96251171511
0.3%
0.87956888471
0.3%
0.82544517341
0.3%

Group8
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct327
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2603933647
Minimum0
Maximum1
Zeros6
Zeros (%)1.8%
Memory size2.8 KiB
2021-02-06T08:18:50.180546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02738991054
Q10.1043861829
median0.1974403579
Q30.3952534791
95-th percentile0.6208996024
Maximum1
Range1
Interquartile range (IQR)0.2908672962

Descriptive statistics

Standard deviation0.1957025973
Coefficient of variation (CV)0.7515652233
Kurtosis0.819527465
Mean0.2603933647
Median Absolute Deviation (MAD)0.1269383698
Skewness0.9954004001
Sum88.01295726
Variance0.03829950658
MonotocityNot monotonic
2021-02-06T08:18:50.313807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
1.8%
0.5183896622
 
0.6%
0.080268389662
 
0.6%
0.28717693842
 
0.6%
0.14234592452
 
0.6%
0.095626242542
 
0.6%
0.32117296222
 
0.6%
0.32892644141
 
0.3%
0.43653081511
 
0.3%
0.32987077531
 
0.3%
Other values (317)317
93.8%
ValueCountFrequency (%)
06
1.8%
0.0092047713721
 
0.3%
0.011948310141
 
0.3%
0.014532803181
 
0.3%
0.014617296221
 
0.3%
ValueCountFrequency (%)
11
0.3%
0.93339960241
0.3%
0.91699801191
0.3%
0.90606361831
0.3%
0.80516898611
0.3%

Group9
Real number (ℝ≥0)

Distinct286
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3583972806
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:50.448409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1235451148
Q10.2514682328
median0.3457020822
Q30.426988788
95-th percentile0.6557928457
Maximum1
Range1
Interquartile range (IQR)0.1755205553

Descriptive statistics

Standard deviation0.1557753187
Coefficient of variation (CV)0.4346442543
Kurtosis1.296573185
Mean0.3583972806
Median Absolute Deviation (MAD)0.08756006407
Skewness0.7800505461
Sum121.1382808
Variance0.02426594993
MonotocityNot monotonic
2021-02-06T08:18:50.583676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32140950354
 
1.2%
0.25146823283
 
0.9%
0.38761345443
 
0.9%
0.41964762413
 
0.9%
0.29364655633
 
0.9%
0.34276561673
 
0.9%
0.30539241862
 
0.6%
0.18633208762
 
0.6%
0.38013881472
 
0.6%
0.35344367332
 
0.6%
Other values (276)311
92.0%
ValueCountFrequency (%)
01
0.3%
0.025627335821
0.3%
0.07367859051
0.3%
0.075280298991
0.3%
0.090763481051
0.3%
ValueCountFrequency (%)
11
0.3%
0.92845702081
0.3%
0.84143085961
0.3%
0.80672717571
0.3%
0.79444741061
0.3%

Group10
Real number (ℝ≥0)

Distinct314
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2717681191
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:50.719061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07193555181
Q10.162331508
median0.2391533277
Q30.3433550969
95-th percentile0.5702506318
Maximum1
Range1
Interquartile range (IQR)0.1810235889

Descriptive statistics

Standard deviation0.1552122642
Coefficient of variation (CV)0.5711202062
Kurtosis2.318533108
Mean0.2717681191
Median Absolute Deviation (MAD)0.08814237574
Skewness1.221422123
Sum91.85762426
Variance0.02409084697
MonotocityNot monotonic
2021-02-06T08:18:50.854164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14132266223
 
0.9%
0.23525695033
 
0.9%
0.19187026123
 
0.9%
0.32518955352
 
0.6%
0.18323504632
 
0.6%
0.22872788542
 
0.6%
0.37615838252
 
0.6%
0.14279696712
 
0.6%
0.12320977252
 
0.6%
0.23862679022
 
0.6%
Other values (304)315
93.2%
ValueCountFrequency (%)
01
0.3%
0.0058972198821
0.3%
0.0061078348781
0.3%
0.01010951981
0.3%
0.021061499581
0.3%
ValueCountFrequency (%)
11
0.3%
0.90564448191
0.3%
0.83909014321
0.3%
0.78917438921
0.3%
0.68765796121
0.3%

Group11
Real number (ℝ≥0)

Distinct332
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1135731195
Minimum0
Maximum1
Zeros1
Zeros (%)0.3%
Memory size2.8 KiB
2021-02-06T08:18:50.973480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02142183704
Q10.04486528629
median0.08006672227
Q30.1488287341
95-th percentile0.3138031693
Maximum1
Range1
Interquartile range (IQR)0.1039634478

Descriptive statistics

Standard deviation0.1024935674
Coefficient of variation (CV)0.9024456477
Kurtosis16.91705187
Mean0.1135731195
Median Absolute Deviation (MAD)0.04110309316
Skewness2.904211944
Sum38.3877144
Variance0.01050493136
MonotocityNot monotonic
2021-02-06T08:18:51.130457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.061899408933
 
0.9%
0.066105812822
 
0.6%
0.055517278892
 
0.6%
0.17427566452
 
0.6%
0.02491206442
 
0.6%
0.10516009721
 
0.3%
0.22199659141
 
0.3%
0.035573122531
 
0.3%
0.071653914491
 
0.3%
0.04543641441
 
0.3%
Other values (322)322
95.3%
ValueCountFrequency (%)
01
0.3%
0.0016680567141
0.3%
0.0020669398411
0.3%
0.0054030532691
0.3%
0.0057656742941
0.3%
ValueCountFrequency (%)
11
0.3%
0.50538492221
0.3%
0.42814664391
0.3%
0.42669615981
0.3%
0.39877434091
0.3%

Class
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2
182 
1
156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters338
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
2182
53.8%
1156
46.2%
2021-02-06T08:18:51.362303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-06T08:18:51.421542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2182
53.8%
1156
46.2%

Most occurring characters

ValueCountFrequency (%)
2182
53.8%
1156
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number338
100.0%

Most frequent character per category

ValueCountFrequency (%)
2182
53.8%
1156
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common338
100.0%

Most frequent character per script

ValueCountFrequency (%)
2182
53.8%
1156
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII338
100.0%

Most frequent character per block

ValueCountFrequency (%)
2182
53.8%
1156
46.2%

Interactions

2021-02-06T08:18:36.696802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:36.810755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:36.898848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:36.983640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.067247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.149876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.233541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.325301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.422781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.508473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.592869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.679034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.764112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.847061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:37.933004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.019238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.105010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.187846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.268183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.346774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.425516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.503696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.581188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.659293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.766751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.849493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:38.928996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.007496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.088868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.501126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.601153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.680317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.759660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.837982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.916105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:39.998595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.078452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.157223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.242962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.325562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.404299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.485906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.565925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.644315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.724917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.804978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.882707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:40.963496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.045434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.125312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.204929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.283353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.372359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.475908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.581864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.687951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.790814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.888910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:41.990113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.095475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.199176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.300702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.405457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.497041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.589484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.700062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.800262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.882796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:42.965075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.054120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.154852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.259135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.365627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.472438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.579870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.685953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.786002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.881870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:43.988519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.084841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.181010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.280846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.378839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.477531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.577982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.682195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.784197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.883543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:44.988068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.087051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.171010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.259140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.359734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.461303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.570572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.673891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.779886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.873597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:45.970386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.081663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.195902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.310625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.422893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.538335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.646657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.762667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.877436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:46.994347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:47.103746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-06T08:18:47.189622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-06T08:18:51.481413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-06T08:18:51.638690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-06T08:18:51.817031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-06T08:18:51.984049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-06T08:18:47.352562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-06T08:18:47.541740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Group1Group2Group3Group4Group5Group6Group7Group8Group9Group10Group11Class
00.5210370.0226580.5459890.3640420.6800100.7920370.7031400.7311130.6684460.6055180.3552601
10.6431440.2725740.6157830.5020170.2704710.1817680.2036080.3487570.3443670.1413230.1552741
20.6014960.3902600.5957430.4497980.5729410.4310170.4625120.6356860.4815800.2112470.2285601
30.2100900.3608390.2335010.1029930.9732330.8113610.5656040.5228630.7634811.0000000.1379051
40.6298930.1565780.6309860.4897050.4597880.3478930.4639180.5183900.3427660.1868160.2327661
50.2588390.2025700.2679840.1416260.7943790.4619960.3697280.4020380.4911910.5511790.0794871
60.5333430.3473110.5238750.3805990.3908020.2748910.2640580.3677930.3347570.1571190.1201731
70.3184720.3760570.3207100.1844190.6860930.4451260.2194470.2974650.5493860.5170600.1697791
80.2848690.4095370.3020520.1597540.7882950.5331570.4355670.4648610.6316070.5040020.0692611
90.2593120.4846130.2776590.1411170.6824430.6754800.5325680.4246020.4607580.6838670.0661061

Last rows

Group1Group2Group3Group4Group5Group6Group7Group8Group9Group10Group11Class
3280.4396330.3719990.4361140.2845260.6617590.3451320.3462980.4218690.4169780.2697980.1168361
3290.4391590.4115660.4402600.2900870.6568930.3340900.4215090.3966700.3748000.3235050.1651011
3300.4282740.1961450.4285120.2758230.3942090.3610820.2820990.3499500.3283500.2064030.0804291
3310.2839230.3260060.2814590.1572910.4049150.2856270.1665180.1466200.3171380.3251900.0555172
3320.2006250.3432530.1945270.1035020.5218400.1484880.0117290.0376890.4127070.2173550.0661062
3330.2020450.1714580.1906570.1046490.2500300.0772960.0022810.0146170.3235450.2285170.0359362
3340.2517390.3148460.2355750.1362340.2508820.0694440.0181720.0424200.1986120.1998740.0249122
3350.4770220.3818060.4699740.3290600.6009250.2644620.3533270.4937380.2989860.2264110.2541251
3360.2843960.1528580.2784190.1574190.3906800.2462120.0875820.1042740.2589430.4721990.0239692
3370.5579540.3963480.5466800.4026750.3485830.3705910.2483600.3026840.4196480.2289390.1910651